Last updated 12/2021
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 390.28 MB | Duration: 0h 45m
AWS DataBrew
What you’ll learn
AWS Glue DataBrew
Transformations using DataBrew
Cleanup and Normalize data
Data Profiling
Scheduling DataBrew Jobs
Requirements
No programming experience required. Basic understanding of ETL/ELT and Cloud Architecture is an advantage, but not mandatory
Description
The course is about AWS Glue DataBrew.AWS Glue DataBrew is a new visual data preparation tool that makes it easy for data analysts and data scientists to clean and normalize data to prepare it for analytics and machine learning. You can choose from over 250 pre-built transformations to automate data preparation tasks, all without the need to write any code. You can automate filtering anomalies, converting data to standard formats, and correcting invalid values, and other tasks. After your data is ready, you can immediately use it for analytics and machine learning projects. You only pay for what you use – no upfront commitment.Using DataBrew, business analysts, data scientists, and data engineers can more easily collaborate to get insights from raw data. Because DataBrew is serverless, no matter what your technical level, you can explore and transform terabytes of raw data without needing to create clusters or manage any infrastructure.With the intuitive DataBrew interface, you can interactively discover, visualize, clean, and transform raw data. DataBrew makes smart suggestions to help you identify data quality issues that can be difficult to find and time-consuming to fix. With DataBrew preparing your data, you can use your time to act on the results and iterate more quickly. You can save transformation as steps in a recipe, which you can update or reuse later with other datasets, and deploy on a continuing basis.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Overview of AWS Glue DataBrew
Section 2: Creating Data Set
Lecture 3 Create RDS Data Set
Lecture 4 Create S3 Data Set
Section 3: Filter and Column
Lecture 5 Filter
Lecture 6 Column
Section 4: Format, Clean and Extract
Lecture 7 Format
Lecture 8 Clean
Lecture 9 Extract
Section 5: Missing, Invalid, Duplicates and Outliers
Lecture 10 Missing
Lecture 11 Invalid
Lecture 12 Duplicates
Lecture 13 Outliers
Section 6: Split, Merge and Create
Lecture 14 Split
Lecture 15 Merge
Lecture 16 Create
Section 7: Group, Join and Union
Lecture 17 Group
Lecture 18 Join
Lecture 19 Union
ETL Developers,Data Engineers,Data Architects,Business Analysts
Homepage
https://www.udemy.com/course/aws-glue-databrew/
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